A memetic algorithm for minimizing the makespan in the Job Shop Scheduling problem

The Job Shop Scheduling Problem (JSP) is a combinatorial optimization problem cataloged as type NP-Hard. To solve this problem, several heuristics and metaheuristics have been used. In order to minimize the makespan, we propose a Memetic Algorithm (MA), which combines the exploration of the search space by a Genetic Algorithm (GA), and the exploitation of the solutions using a local search based on the neighborhood structure of Nowicki and Smutnicki. The genetic strategy uses an operation-based representation that allows generating feasible schedules, and a selection probability of the best individuals that are crossed using the JOX operator. The results of the implementation show that the algorithm is competitive with other approaches proposed in the literature

Reduction of carbon emission and total late work criterion in job shop scheduling by applying a multi-objective imperialist competitive algorithm

New environmental regulations have driven companies to adopt low-carbon manufacturing. This research is aimed at considering carbon dioxide in the operational decision level where limited studies can be found, especially in the scheduling area. In particular, the purpose of this research is to simultaneously minimize carbon emission and total late work criterion as sustainability-based and classical-based objective functions, respectively, in the multiobjective job shop scheduling environment. In order to solve the presented problem more effectively, a new multiobjective imperialist competitive algorithm imitating the behavior of imperialistic competition is proposed to obtain a set of non-dominated schedules. In this work, a three-fold scientific contribution can be observed in the problem and solution method, that are: (1) integrating carbon dioxide into the operational decision level of job shop scheduling, (2) considering total late work criterion in multi-objective job shop scheduling, and (3) proposing a new multi-objective imperialist competitive algorithm for solving the extended multi-objective optimization problem. The elements of the proposed algorithm are elucidated and forty three small and large sized extended benchmarked data sets are solved by the algorithm. Numerical results are compared with two well-known and most representative metaheuristic approaches, which are multi-objective particle swarm optimization and non-dominated sorting genetic algorithm II, in order to evaluate the performance of the proposed algorithm. The obtained results reveal the effectiveness and efficiency of the proposed multi-objective imperialist competitive algorithm in finding high quality non-dominated schedules as compared to the other metaheuristic approache

Dynamic Scheduling for Maintenance Tasks Allocation supported by Genetic Algorithms

Since the first factories were created, man has always tried to maximize its production and, consequently, his profits. However, the market demands have changed and nowadays is not so easy to get the maximum yield of it. The production lines are becoming more flexible and dynamic and the amount of information going through the factory is growing more and more. This leads to a scenario where errors in the production scheduling may occur often. Several approaches have been used over the time to plan and schedule the shop-floor’s production. However, some of them do not consider some factors present in real environments, such as the fact that the machines are not available all the time and need maintenance sometimes. This increases the complexity of the system and makes it harder to allocate the tasks competently. So, more dynamic approaches should be used to explore the large search spaces more efficiently. In this work is proposed an architecture and respective implementation to get a schedule including both production and maintenance tasks, which are often ignored on the related works. It considers the maintenance shifts available. The proposed architecture was implemented using genetic algorithms, which already proved to be good solving combinatorial problems such as the Job-Shop Scheduling problem. The architecture considers the precedence order between the tasks of a same product and the maintenance shifts available on the factory. The architecture was tested on a simulated environment to check the algorithm behavior. However, it was used a real data set of production tasks and working stations

Development of a Multi-Objective Scheduling System for Complex Job Shops in a Manufacturing Environment

In many sectors of commercial operation, the scheduling of workflows and the allocation of resources at an optimum time is critical; for effective and efficient operation. The high degree of complexity of a “Job Shop” manufacturing environment, with sequencing of many parallel orders, and allocation of resources within multi-objective operational criteria, has been subject to several research studies. In this thesis, a scheduling system for optimizing multi-objective job shop scheduling problems was developed in order to satisfy different production system requirements. The developed system incorporated three different factors; setup times, alternative machines and release dates, into one model. These three factors were considered after a survey study of multiobjective job shop scheduling problems. In order to solve the multi-objective job shop scheduling problems, a combination of genetic algorithm and a modified version of a very recent and computationally efficient approach to non-dominated sorting solutions, called “efficient non-dominated sort using the backward pass sequential strategy”, was applied. In the proposed genetic algorithm, an operation based representation was designed in the matrix form, which can preserve features of the parent after the crossover operator without repairing the solution. The proposed efficient non-dominated sort using the backward pass sequential strategy was employed to determine the front, to which each solution belongs. The proposed system was tested and validated with 20 benchmark problems after they have been modified. The experimental results show that the proposed system was effective and efficient to solve multi-objective job shop scheduling problems in terms of solution quality

Desafios na aplicação de Particle Swarm Optimization em um problema de planejamento de produção de uma olaria

Orientadora : Profª. Drª. Neida Maria Patias VolpiDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Métodos Numéricos em Engenharia. Defesa: Curitiba, 03/03/2017Inclui referências : p. 75-76Resumo: O presente trabalho tem como objetivo principal verificar a aplicabilidade de uma math-heurística em um problema de Planejamento de Produção de uma indústria de tijolos. Foi escolhida a metaheurística Particle Swarm Optimization (PSO) para a aplicação no problema. Depois de realizada uma revisão bibliográfica acerca do assunto percebeu-se que são encontrados poucos trabalhos que aplicam um PSO discreto em um problema de planejamento de produção, o que motivou o trabalho. A math-heurística será aqui a combinação do PSO com a metodologia exata Branch-and-Bound. A combinação deve-se ao fato de que a aplicação proposta é feita com um PSO discreto, ou seja, a matriz possui somente entradas inteiras. O uso do PSO discreto fez com que fosse proposta a utilização de uma metodologia exata para que a função objetivo do problema fosse encontrada, sendo que esta depende da distribuição das quantidades a serem produzidas e esses valores, por sua vez, são encontrados com base na matriz discreta que está sendo movimentada pelo PSO. O problema proposto possui preservação da preparação, capacidade (tempo limite de produção) e demanda a ser atendida. A preservação da preparação é o que dificulta a resolução do problema por heurísticas (como o Greedy-Mod) e também faz com que se encontrem muitas infactibilidades. Os desafios encontrados na aplicação dessa proposta não foram poucos. Merece uma maior discussão a codificação e representação das soluções, tratamento de infactibilidades nas soluções iniciais e na movimentação da nuvem. Estes problemas geraram alto custo computacional. Sugere-se uma tentativa de mudança na forma de aplicação (para a utilização de um PSO contínuo), de forma que as infactibilidades sejam reduzidas e o tempo computacional melhorado. Palavras-chaves: PSO. Planejamento de Produção. Olaria. Metodologia exata. Branch-and- Bound.Abstract: The present work has as main objective to verify the applicability of a math-heuristic in the Production Planning of a brick industry. The Particle Swarm Optimization (PSO) metaheuristic was chosen for the application in the problem. After a bibliographical review about the subject, it was noticed that not too many works were found that apply a discrete PSO in a problem of production planning, one of the reasons that motivated this work. The math-heuristic will be the combination of the metaheuristic PSO with the Branch-and-Bound exact methodology (performed by an optimizer). The combination is due to the fact that the proposed application is made with a discrete PSO, that is, the matrix to be moved has only discrete inputs. The use of discrete PSO has meant that the use of an exact methodology is proposed so that the objective function of the problem is found, which depends on the distribution of the quantities to be produced and these values, in turn, are found based on the discrete matrix that is being moved by the PSO. The proposed problem has preservation of the preparation, capacity (time limit of production) and demand to be met. Preservation of the preparation is what makes it difficult to solve the problem by heuristics (such as Greedy-Mod) and also causes many infeasibilities. Deserves a further discussion the codification and representation of solutions, treatment of infeasibilities in the initial solutions and in the movement of the swarm. These problems generated a high computational cost. An attempt is made to change the manner of application (for the use of a continuous PSO), so that the infeasibilities are reduced and the computational time improved. Key-words: PSO. Production Planning. Brickyard. Exact methodology. Branch-and-Bound

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

Algorithms based on spider daddy long legs for finding the optimal route in securing mobile ad hoc networks

Mobile ad hoc networks (MANETs) are wireless networks that are subject to severe attacks, such as the black hole attack. One of the goals in the research is to find a method to prevent black hole attacks without decreasing network throughput or increasing routing overhead. The routing mechanism in define uses route requests (RREQs; for discovering routes) and route replies (RREPs; for receiving paths). However, this mechanism is vulnerable to attacks by malicious black hole nodes. The mechanism is developed to find the shortest secure path and to reduce overhead using the information that is available in the routing tables as an input to propose a more complex nature-inspired algorithm. The new method is called the Daddy Long-Legs Algorithm (PGO-DLLA), which modifies the standard AODV and optimizes the routing process. This method avoids dependency exclusively on the hop counts and destination sequence numbers (DSNs) that are exploited by malicious nodes in the standard AODV protocol. The experiment by performance metrics End-to-End delay and packet delivery ratio are compared in order to determine the best effort traffic. The results showed the PGO-DLLA improvement of the shortest and secure routing from black hole attack in MANET. In addition, the results indicate better performance than the related works algorithm with respect to all metrics excluding throughput which AntNet is best in routing when the pause time be more than 40 seconds. PGODLLA is able to improve the route discovery against the black hole attacks in AODV. Experiments in this thesis have shown that PGO-DLLA is able to reduce the normalized routing load, end-to-end delay, and packet loss and has a good throughput and packet delivery ratio when compared with the standard AODV protocol, BAODV protocol, and the current related protocols that enhance the routing security of the AODV protocols

Sessenta anos de Shop Scheduling : uma revisão sistemática da literatura

Orientador : Prof. Dr. Cassius Tadeu ScarpinDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia de Produção. Defesa: Curitiba, 09/02/2017Inclui referências : f. 449-492Resumo: Desde o seminal artigo de Johnson em 1954, a Programação da Produção em Shop Scheduling tem se tornado uma área relevante dentro da Pesquisa Operacional e, atualmente, duzentos trabalhos tangentes à temática são publicados anualmente. Dentre os artigos aqui citados tem-se aqueles que se dedicam à apresentação e síntese do estado da arte desse assunto, intitulados artigos de revisão. Quando tais artigos são elaborados a partir de um conjunto objetivo de critérios, relativos à categorização dos artigos selecionados, tem-se a Revisão Sistemática da Literatura (RSL). O presente trabalho realiza uma RSL em Shop Scheduling, a partir da análise de cada ambiente fabril que o compõe. Fez-se o escrutínio de 560 artigos, à luz de um conjunto de métricas, que constitui a estrutura basilar da proposta de nova taxonomia do Shop Scheduling, complementar à notação de Graham, objetivo fulcral do presente trabalho. Além disso, utilizou-se uma representação em redes dos resultados obtidos em algumas das métricas empregadas, como a característica dos itens, algo outrora inaudito em estudos de revisão desse assunto. Ademais, outro ponto relevante desse estudo repousa na identificação de campos pouco explorados, de modo a colaborar com a pesquisa futura neste tomo. Palavras-chave: Shop Scheduling. Revisão Sistemática da Literatura. Taxonomia. Representação em Redes.Abstract: Since Johnson's seminal article in 1954, Shop Scheduling in Production Scheduling has become a relevant area within Operational Research, and currently hundreds of tangential works on the subject are published annually. Among the articles cited here are those dedicated to the presentation and synthesis of the state of the art of this subject, which are entitled review articles. When these articles are elaborated from an objective set of criteria, regarding the categorization of the selected articles, we have the Systematic Review of Literature (SLR). The present work performs a SLR in Shop Scheduling, based on the analysis of each manufacturing environment that composes it. There were 560 articles scrutinized based on a set of metrics, which is the basic structure of the proposed new Taxonomy of Shop Scheduling, complementary to Graham's notation, the main objective of this work. In addition to that a network representation of the results was obtained in some of the metrics used, such as the job characteristics, something previously unheard of in review studies of this subject. Moreover, another relevant point of this study lies in the identification of less explored fields in order to collaborate with future research in this matter. Keywords: Shop Scheduling. Systematic Literature Review. Taxonomy. Network Representation

Metodología multiobjetivo basada en un comportamiento evolutivo para programar sistemas de producción flexible job shop. Aplicaciones en la industria metalmecánica

El objeto de estudio de la presente tesis es el taller de trabajo flexible en el sector metalmecánico. El problema de investigación se derivó a partir de la búsqueda sistemática de metodologías y algoritmos para programar sistemas productivos; se identificaron configuraciones de variables de proceso no abordadas en la literatura, lo que se considera un vacío en el conocimiento. Consecuente con lo anterior, se diseñó una metodología basada en un algoritmo evolutivo para programar los pedidos en un taller de trabajo flexible, con restricciones de tiempo, secuencia, mantenimiento, liberación de pedidos, disponibilidad, consumo y costo de recurso que varía en el tiempo, con el fin de minimizar tiempo de proceso y costo de producción; incluyó un proceso de ponderación para escoger la mejor secuencia de programación. Como aporte principal se propone una metodología novedosa que al compararla con otras metodologías encontradas en la bibliografía, demostró mejoras mayores al 10% en makespan y costo total del recurso consumidoAbstract: The study object of the present thesis is the flexible job shop in the metal mechanic sector. The research problem was derived from the systematic search of methodologies and algorithms to schedule production systems; configurations of process variables not addressed in the literature were identified, which is considered an empty in knowledge. Consequent with previous, a methodology was designed based on an evolutionary algorithm to schedule orders in a flexible job shop, with time restrictions, sequence, maintenance, liberation of orders, availability, consumption and cost of resource that varies in time, in order to minimize processing time and cost of production; it includes a weighting process to choose the best programming sequence. As main contribution a novel methodology was proposed which, compared with other methodologies found in the literature, it demonstrated greater improvements to 10% in Makespan and total cost of consumed resourceDoctorad